Journal Article10.1016/J.ENGAPPAI.2015.07.020
Fault detection with Conditional Gaussian Network
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TL;DR: The main interest of this paper is to illustrate a new representation of the Principal Component Analysis for fault detection under a Conditional Gaussian Network (CGN), a special case of Bayesian networks.
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About: This article is published in Engineering Applications of Artificial Intelligence. The article was published on 01 Oct 2015. The article focuses on the topics: Fault detection and isolation & Bayesian network.
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Pattern Recognition and Machine Learning
Christopher M. Bishop
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TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
10.1K
Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
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Bayesian networks and decision graphs
Finn B. Jensen,Thomas Graven-Nielsen +1 more
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TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.